摘要
针对传统手写数字识别方法识别率较低的问题,提出一种融合卷积神经网络(F-CNN)模型。通过结合暹罗网络(SN)模型和二进制卷积神经网络(B-CNN)模型的高级特征,扩展网络高级层的尺寸,增强F-CNN模型的特征表达能力。在网络训练过程中,设计周期性数据打乱策略,提高F-CNN模型的收敛速度,更好地实现手写数字识别。在MNIST数据集上的实验结果表明,融合模型对于手写数字的识别准确率达到99.10%,识别性能优于SN模型和B-CNN模型。
Aiming at the problem that the recognition rate of traditional handwritten digits recognition method is low,this paper proposes a Fused Convolutional Neural Network（ F-CNN） model. By combining the high-level features of the Siamese Network（ SN） model and Binary Convolutional Neural Network（ B-CNN） model,the F-CNN model expands the size of the high-level layers and enhances the features-expression ability of deep CNN network model. In the process of network training,a kind of periodic data shuffle strategy is designed to improve the convergence rate of the F-CNN model to realize better handwritten digits recognition. Experiments results on the public MNIST dataset show that the proposed F-CNN model has 99. 10% recognition rate for handwritten digits,which outperforms the SN model and the B-CNN model.
出处
《计算机工程》
CAS
CSCD
北大核心
2017年第11期187-192,共6页
Computer Engineering
基金
中央高校基本科研业务费专项资金(2042016gf0033)
武汉市应用基础研究计划项目(2016010101010025)
关键词
手写数字
融合模型
卷积神经网络
数据打乱策略
收敛速度
handwritten digits
fused model
Convolutional Neural Network （CNN）
data scrambling strategy
convergence rate
